Mitochondrial Cox1 Sequence Data Reliably Uncover Patterns of Insect Diversity But Suffer from High Lineage-Idiosyncratic Error Rates
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{"title"=>"Mitochondrial Cox1 Sequence Data Reliably Uncover Patterns of Insect Diversity But Suffer from High Lineage-Idiosyncratic Error Rates", "type"=>"journal", "authors"=>[{"first_name"=>"Lars", "last_name"=>"Hendrich", "scopus_author_id"=>"6603601867"}, {"first_name"=>"Joan", "last_name"=>"Pons", "scopus_author_id"=>"7202123136"}, {"first_name"=>"Ignacio", "last_name"=>"Ribera", "scopus_author_id"=>"7004248462"}, {"first_name"=>"Michael", "last_name"=>"Balke", "scopus_author_id"=>"7004150795"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"21203427", "scopus"=>"2-s2.0-78650830276", "doi"=>"10.1371/journal.pone.0014448", "sgr"=>"78650830276", "pui"=>"361034494"}, "id"=>"2fc4e6cf-1fc5-381a-ad58-8ef894830e72", "abstract"=>"<sec> <title>Background</title> <p>The demand for scientific biodiversity data is increasing, but taxonomic expertise is often limited or not available. DNA sequencing is a potential remedy to overcome this <italic>taxonomic impediment</italic>. Mitochondrial DNA is most commonly used, e.g., for species identification (“DNA barcoding”). Here, we present the first study in arthropods based on a near-complete species sampling of a family-level taxon from the entire Australian region. We aimed to assess how reliably mtDNA data can capture species diversity when many sister species pairs are included. Then, we contrasted phylogenetic subsampling with the hitherto more commonly applied geographical subsampling, where sister species are not necessarily captured.</p> </sec><sec> <title>Methodology/Principal Findings</title> <p>We sequenced 800 bp <italic>cox1</italic> for 1,439 individuals including 260 Australian species (78% species coverage). We used clustering with thresholds of 1 to 10% and general mixed Yule Coalescent (GMYC) analysis for the estimation of species richness. The performance metrics used were <italic>taxonomic accuracy</italic> and <italic>agreement</italic> between the morphological and molecular species richness estimation. Clustering (at the 3% level) and GMYC reliably estimated species diversity for single or multiple geographic regions, with an error for larger clades of lower than 10%, thus outperforming parataxonomy. However, the rates of error were higher for some individual genera, with values of up to 45% when very recent species formed nonmonophyletic clusters. <italic>Taxonomic accuracy</italic> was always lower, with error rates above 20% and a larger variation at the genus level (0 to 70%). Sørensen similarity indices calculated for morphospecies, 3% clusters and GMYC entities for different pairs of localities was consistent among methods and showed expected decrease over distance.</p> </sec><sec> <title>Conclusion/Significance</title> <p><italic>Cox1</italic> sequence data are a powerful tool for large-scale species richness estimation, with a great potential for use in ecology and β-diversity studies and for setting conservation priorities. However, error rates can be high in individual lineages.</p> </sec>", "link"=>"http://www.mendeley.com/research/mitochondrial-cox1-sequence-data-reliably-uncover-patterns-insect-diversity-suffer-high-lineageidios", "reader_count"=>97, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>27, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>8, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>5, "Librarian"=>1, "Researcher"=>27, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>3, "Student > Master"=>15, "Other"=>3, "Student > Bachelor"=>8, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Environmental Science"=>6, "Biochemistry, Genetics and Molecular Biology"=>5, "Agricultural and Biological Sciences"=>82, "Medicine and Dentistry"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>82}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>6}}, "reader_count_by_country"=>{"Canada"=>2, "Papua New Guinea"=>1, "United States"=>1, "Finland"=>1, "United Kingdom"=>1, "France"=>2, "Germany"=>1, "Spain"=>4, "India"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/809635"], "description"=>"<p>Blue lines, <i>agreement</i> derived from clustering <b>(percentage of clusters relative to number of morphological species used in the particular dataset)</b>, and orange lines, <i>taxonomic accuracy</i> of clusters <b>(number of clusters containing all sequences of a named species and only those)</b>. (A) Full dataset and two modifications thereof (315 species: dashed lines; 260 species: dotted; 242 species: solid lines); (B) all Hydroporini; (C) Hydroporini: <i>Sternopriscus</i>; (D) Hydroporini: <i>Tiporus</i>; (E) Hydroporini: <i>Megaporus</i>; (F) Copelatinae: <i>Exocelina</i>, purple and green – agreement and taxonomic accuracy for the raw dataset, blue and orange – taxonomically revised dataset. Circles - <i>agreement</i> for GMYC entities, triangles – <i>taxonomic accuracy</i> of GMYC entities (in F, purple and green GMYC for taxonomically raw <i>Exocelina</i> dataset).</p>", "links"=>[], "tags"=>["molecular", "clusters", "thresholds", "dna", "divergence", "gmyc"], "article_id"=>480000, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.g002", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Agreement_and_taxonomic_accuracy_of_molecular_clusters_estimated_with_different_thresholds_of_DNA_sequence_divergence_and_the_GMYC_algorithm_/480000", "title"=>"<i>Agreement</i> and <i>taxonomic accuracy</i> of molecular clusters estimated with different thresholds of DNA sequence divergence and the GMYC algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-28 00:00:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/404114", "https://ndownloader.figshare.com/files/404137"], "description"=>"<div><h3>Background</h3><p>The demand for scientific biodiversity data is increasing, but taxonomic expertise is often limited or not available. DNA sequencing is a potential remedy to overcome this <em>taxonomic impediment</em>. Mitochondrial DNA is most commonly used, e.g., for species identification (“DNA barcoding”). Here, we present the first study in arthropods based on a near-complete species sampling of a family-level taxon from the entire Australian region. We aimed to assess how reliably mtDNA data can capture species diversity when many sister species pairs are included. Then, we contrasted phylogenetic subsampling with the hitherto more commonly applied geographical subsampling, where sister species are not necessarily captured.</p><h3>Methodology/Principal Findings</h3><p>We sequenced 800 bp <em>cox1</em> for 1,439 individuals including 260 Australian species (78% species coverage). We used clustering with thresholds of 1 to 10% and general mixed Yule Coalescent (GMYC) analysis for the estimation of species richness. The performance metrics used were <em>taxonomic accuracy</em> and <em>agreement</em> between the morphological and molecular species richness estimation. Clustering (at the 3% level) and GMYC reliably estimated species diversity for single or multiple geographic regions, with an error for larger clades of lower than 10%, thus outperforming parataxonomy. However, the rates of error were higher for some individual genera, with values of up to 45% when very recent species formed nonmonophyletic clusters. <em>Taxonomic accuracy</em> was always lower, with error rates above 20% and a larger variation at the genus level (0 to 70%). Sørensen similarity indices calculated for morphospecies, 3% clusters and GMYC entities for different pairs of localities was consistent among methods and showed expected decrease over distance.</p><h3>Conclusion/Significance</h3><p><em>Cox1</em> sequence data are a powerful tool for large-scale species richness estimation, with a great potential for use in ecology and β-diversity studies and for setting conservation priorities. However, error rates can be high in individual lineages.</p></div>", "links"=>[], "tags"=>["mitochondrial", "reliably", "patterns", "lineage-idiosyncratic", "rates"], "article_id"=>139859, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0014448.s001", "https://dx.doi.org/10.1371/journal.pone.0014448.s002"], "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Mitochondrial_Cox1_Sequence_Data_Reliably_Uncover_Patterns_of_Insect_Diversity_But_Suffer_from_High_Lineage_Idiosyncratic_Error_Rates/139859", "title"=>"Mitochondrial <em>Cox1</em> Sequence Data Reliably Uncover Patterns of Insect Diversity But Suffer from High Lineage-Idiosyncratic Error Rates", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-12-28 02:44:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/810017"], "description"=>"<p>*dataset was taxonomically cleaned.</p><p>For GMYC analysis, dataset modified, identical and near-identical haplotypes removed.</p>", "links"=>[], "tags"=>["clustering", "preset", "gmyc"], "article_id"=>480390, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.t002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Agreement_and_taxonomic_accuracy_using_clustering_at_a_preset_threshold_of_3_single_threshold_GMYC_analysis_and_statistical_parsimony_/480390", "title"=>"<i>Agreement</i> and <i>taxonomic accuracy</i> using clustering at a preset threshold of 3%, single threshold GMYC analysis and statistical parsimony.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-28 00:06:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/809958"], "description"=>"<p>Sorensen index for species numbers based on morphology (green), numbers of <i>cox1</i> clusters, estimated at 3% threshold of genetic DNA distance (red) and derived from GMYC algorithm (orange).</p>", "links"=>[], "tags"=>["geographic", "pairwise", "comparisons"], "article_id"=>480321, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.g005", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_geographic_distance_and_946_diversity_S_248_rensen_index_for_pairwise_comparisons_between_localities_/480321", "title"=>"Relation between geographic distance and β-diversity (Sørensen index) for pairwise comparisons between localities.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-28 00:05:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/809869"], "description"=>"<p>(A) Geographical origin of sequenced Australian individuals (green stars), red  =  specimens in paraphyletic clusters. (B) Molecular biodiversity estimation employed for regional comparison. Arrows  =  states compared; Numbers  =  number of clusters using 3% threshold for all samples from the two areas compared (N of clusters shared between two areas) % of clusters that perfectly agree with existing taxonomy; S = Sørensen Index.</p>", "links"=>[], "tags"=>["samples"], "article_id"=>480240, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.g004", "stats"=>{"downloads"=>2, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Geographical_origin_of_samples_and_regional_comparisons_/480240", "title"=>"Geographical origin of samples and regional comparisons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-28 00:04:00"}
  • {"files"=>["https://ndownloader.figshare.com/files/810049"], "description"=>"<p>Abbreviations: TAS  =  Tasmania, NT  =  Northern Territory, VIC  =  Victoria, wa  =  Western Australia, QLD  =  Queensland, NSW  =  New South Wales, SA  =  South Australia.</p>", "links"=>[], "tags"=>["whole-fauna", "clustering"], "article_id"=>480417, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.t001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Agreement_and_taxonomic_accuracy_in_regional_and_whole_fauna_clustering_at_3_/480417", "title"=>"<i>Agreement</i> and <i>taxonomic accuracy</i> in regional and whole-fauna clustering at 3%.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-12-28 00:06:57"}
  • {"files"=>["https://ndownloader.figshare.com/files/809749"], "description"=>"<p>Blue lines: <i>agreement</i> between cluster and morphospecies number, orange lines: <i>taxonomic accuracy</i>. NT, Northern Territory, SA, South Australia, TAS, Tasmania, WA, Western Australia.</p>", "links"=>[], "tags"=>["thresholds", "dna", "clustering", "subsampling", "states", "pairwise"], "article_id"=>480115, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Agreement_and_taxonomic_accuracy_at_different_thresholds_of_genetic_DNA_distance_clustering_using_regional_subsampling_single_states_and_pairwise_comparisons_and_number_of_species_in_the_region_s_sampled_/480115", "title"=>"<i>Agreement</i> and <i>taxonomic accuracy</i> at different thresholds of genetic DNA distance clustering using regional subsampling (single states and pairwise comparisons), and number of species in the region(s) sampled.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-28 00:01:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/809520"], "description"=>"<p>Focal clades are Hydroporini (blue, polyphyletic); <i>Neobidessodes</i> (green) and <i>Exocelina</i> (orange). Three pink clades contain paraphyletic species. Dots denote speciation events as inferred from morphospecies identification or combined genetic and morphological data. Note: Multiple dots within the pink clades omitted for clarity.</p>", "links"=>[], "tags"=>["cox1", "sequences", "australian", "diving", "beetle", "fauna", "ml", "lengths", "ultrametric", "relaxed", "molecular"], "article_id"=>479873, "categories"=>["Ecology", "Evolutionary Biology"], "users"=>["Lars Hendrich", "Joan Pons", "Ignacio Ribera", "Michael Balke"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0014448.g001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Chronogram_of_cox1_sequences_for_the_Australian_diving_beetle_fauna_based_on_ML_branch_lengths_which_were_made_ultrametric_with_a_relaxed_molecular_clock_/479873", "title"=>"Chronogram of cox1 sequences for the Australian diving beetle fauna based on ML branch lengths which were made ultrametric with a relaxed molecular clock.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-12-28 02:44:33"}

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Relative Metric

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